Welcome to the 2022 workshop on Bayesian Statistics Using R. This page will be used to distribute the slides, handouts, and other materials for this course. All material is free to re-use under the CC-BY license.
In this workshop, you will receive an in-depth introduction to the
Bayesian Framework of Data Analysis. You will learn about the infamous
“Bayes Theorem” and how it can be used for “Bayesian Inference”. More
practically, you will further learn how to run (generalized) linear
models using a Bayesian framework as implemented in the R package
brms (Bürckner,
2017). You will learn how to specify models, appropriate priors, and how
to interpret respective results. Thereby we will get to know different
methods to draw probabilistic inferences from posterier distributions,
moving beyond standard frequentist null hypothesis testing.
For this 2-day workshop, I expect you…
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to have R and RStudio installed. If you haven’t done so yet, have a look at this getting started tutorial, which walks you through the installation and helps you get some first hands-on experience using R. You do not need to install the packages that we will use in the workship yet. We will install those together in the workshop.
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to have some basic knowledge of R and particularly data wrangling skills (at best, some knowledge about the
tidyverse). If this is still new to you, we have several videos and tutorials on our R course material GitHub page that can help you getting started. I would suggest to check out the tutorials on transforming, summarizing, visualizing and reshaping data in the “data wrangling with the tidyverse” category. -
a good understanding of descriptive statistics (distributions, parameters of central tendency) and frequentist inference (standard error, p-value, confidence intervals).
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some basic knowledge of regression models (e.g., linear regression, logistic regression, multilevel linear regression). In the practical part of the workshop, we will fit such models in R.
Note: The workshop will NOT provide an introduction to R!
| Time | Topic |
|---|---|
| 9:00 - 9:30: | Welcome |
| 9:30 - 10:30: | An Introductory Example |
| 10:30 - 12:00: | Basics of Bayesian Statistics |
| 12:00 - 13:00: | R: Exercise I |
| 13:00 - 14:00: | Lunch Break |
| 14:00 - 14:45: | Markov Chain Monte Carlo |
| 14:45 - 15:30: | Bayesian Inference vs. NHST |
| 15:30 - 16:00: | Coffee Break |
| 16:00 - 17:00: | R: Exercise II |
| Time | Topic |
|---|---|
| 9:00 - 10:00: | Subjective Beliefs and Knowledge Cumulation |
| 10:00 - 10:30: | Short recap of Day 1 |
| 10:30 - 11:30: | Simple and Multiple Regression |
| 11:30 - 13:00: | R: Exercise III |
| 13:00 - 14:00: | Lunch Break |
| 14:00 - 14:45: | Multilevel Regression |
| 14:45 - 15:30: | R: Exercise IV |
| 15:30 - 16:00: | Coffee Brak |
| 16:00 - 17:00: | Q&A |